Post Doctoral Scholar

at  The Ohio State University Wexner Medical Center

We are seeking a highly motivated and skilled Post Doctoral Scholar in Computational Pathology to join our interdisciplinary team. The successful candidate will work at the intersection of pathology and artificial intelligence, developing cutting-edge deep learning algorithms to analyze pathology images and solve complex biomedical challenges. This is a full-time, on-site position that requires a strong background in deep learning, AI, and related fields.
The ideal candidate will have a PhD in a relevant discipline, such as computer science, electrical engineering, computer engineering, biomedical engineering, bioinformatics, or a related field, with a deep understanding of deep learning algorithms and their applications in healthcare. The PostDoc will be expected to contribute to research projects, assist in grant writing, and collaborate closely with PhD students, clinicians, and other researchers.

MINIMUM REQUIRED QUALIFICATIONS

PhD in computer science, electrical engineering, computer engineering, biomedical engineering, bioinformatics, computational biology, or a related field. Strong expertise in deep learning algorithms, particularly for image analysis (e.g., convolutional neural networks, transfer learning, generative models). Experience in the field of computational pathology or medical imaging. Proficiency in programming languages such as Python and deep learning frameworks such as TensorFlow, PyTorch, or Keras.
Strong publication record demonstrating expertise in deep learning and computational methods. Ability to work collaboratively in a multidisciplinary team environment and provide mentorship to students. Experience with large-scale datasets, particularly pathology image data (e.g., WSI, histopathology images), is a plus. Excellent written and verbal communication skills for scientific writing and presentations.

PREFERRED SKILLS

Experience with cloud computing or high-performance computing environments for training deep learning models. Knowledge of digital pathology workflows and integration of machine learning models into clinical applications. Familiarity with grant writing and the process of securing research funding.
Term (Fixed Term) 40 First Shif

Research & Development
Develop, implement, and optimize deep learning methods for analyzing pathology images, particularly whole slide images (WSI), with applications in disease diagnosis, prognosis, and treatment planning.
Algorithm Design
Design and evaluate novel deep learning algorithms for image segmentation, feature extraction, classification, and multimodal data integration. Stay current with advancements in AI, computational pathology, and medical imaging.
Collaboration
Work closely with PhD students, providing mentorship, guidance, and technical support. Participate in interdisciplinary collaborations with pathologists, biostatisticians, and other researchers to integrate computational models with clinical data.
Grant Writing & Publication
Contribute to grant writing and securing external funding by drafting research proposals and reports. Lead and co-author scientific publications in high-impact journals and present findings at national and international conferences.
Data Management & Analysis
Manage large datasets, including whole slide pathology images and patient data. Ensure the integrity of the data pipeline, from preprocessing to model deployment. Analyze experimental results and interpret them in the context of biological and clinical relevance.
Mentorship
Provide technical support and mentorship to graduate students, guiding them in computational methods, experiment design, and manuscript preparation.
Collaborative Projects
Engage in collaborative projects with internal and external partners, ensuring the seamless integration of computational models into larger research initiatives and clinical workflows.
Software Development
Develop reusable and scalable software tools to automate various tasks in pathology image analysis. Collaborate with the software development team to ensure robust implementation of research ideas.
Contributions to Lab Growth
Actively participate in lab meetings, journal clubs, and research discussions. Contribute to the lab’s growth by bringing innovative ideas, new techniques, and fostering a collaborative research environment